Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
Journal of Machine Learning Research , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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FastSinkhorn delivers a stable CUDA log-domain Sinkhorn solver achieving 12x speedup over POT and 5.9x over PyTorch baselines on n=m=8192 problems while using only 256 MB GPU memory.
citing papers explorer
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An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
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Fast Log-Domain Sinkhorn Optimal Transport with Warp-Level GPU Reductions
FastSinkhorn delivers a stable CUDA log-domain Sinkhorn solver achieving 12x speedup over POT and 5.9x over PyTorch baselines on n=m=8192 problems while using only 256 MB GPU memory.